Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imaging

Jin Keun Seo
Yonsei University
Computational Science & Engineering

The significant recent developments in deep learning have made it relevant to solving underdetermined inverse problems, a major concern in medical imaging. Typical examples where deep learning techniques showed excellent performance include undersampled magnetic resonance imaging, local tomography, and sparse view computed tomography. Although these methods appear to overcome the limitations of existing mathematical methods in handling underdetermined problems, the rigorous mathematical foundations underpinning the success of deep learning remain unknown. This talk deals with the causal relationship between the structure of training data and the ability of deep learning to solve highly underdetermined inverse problems. 

Presentation (PDF File)

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